Data Science Final Project

Graphs

Relationship between Educational Attainment and GDP per Capita

Relationship between Educational Attainment and GDP per Capita by Continent

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    Please upgrade via install.packages('htmlwidgets').

Relationship between Educational Attainment and Child Mortality Rate by Continent

Relationship between Educational Attainment and Number of Workers in Family

Timeline Graph 1

primary_school %>%
  group_by(continent, year) %>%
  summarize(average = mean(primary_school)) %>%
  ggplot(aes(x=year,y=average, color=continent)) + 
  geom_point() + 
  geom_line() + 
  labs(x="Year", y="Average Rate of Educational Attainment", 
       title = "Educational Attainment over Time by Continent")

Map of World Educational Attainment

world <- primary_school %>%
  distinct(country)

map.world <- map_data("world")

world %>% 
  ggplot() +
  geom_map(data = map.world, map = map.world, 
           aes(map_id = region), fill = "white", color = "black") + 
  geom_map(aes(map_id = country), map = map.world) +
  expand_limits(x = map.world$long, y = map.world$lat) + # scale for fill
  theme_map() 

worldmap <- get_stamenmap(
    bbox = c(left = -180, bottom = -57, right = 179, top = 82.1), 
    maptype = "toner-background",
    zoom = 2
)

Source : http://tile.stamen.com/toner-background/2/0/0.png
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Source : http://tile.stamen.com/toner-background/2/0/1.png
Source : http://tile.stamen.com/toner-background/2/1/1.png
Source : http://tile.stamen.com/toner-background/2/2/1.png
Source : http://tile.stamen.com/toner-background/2/3/1.png
Source : http://tile.stamen.com/toner-background/2/0/2.png
Source : http://tile.stamen.com/toner-background/2/1/2.png
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Source : http://tile.stamen.com/toner-background/2/3/2.png

ggmap(worldmap)

primary_school %>%
  filter(decade == 1990) %>%
  group_by(decade, country) %>%
  summarize(average = mean(child_mortality)) %>%
  arrange(desc(average)) %>%
  filter(country==c("Niger", "Costa Rica")) 

longer object length is not a multiple of shorter object length

Tables

Mean Educational Attainment by Continent

primary_school <- primary_school %>%
  mutate(high_gdp = gdp_capita > 3955) 

# scatterplots to find interactions between variables
ggplot(primary_school, aes(x=gdp_capita, y=unemployment, color=high_gdp)) + geom_point()

# linear regression
library(broom)
tidy(lm(primary_school ~ high_gdp + unemployment, data=primary_school))

tidy(lm(primary_school ~ gdp_capita + unemployment + child_mortality, data=primary_school))

tidy(lm(primary_school ~ gdp_capita + unemployment + rural_pop, data=primary_school))

# an interaction effect will let one variable affect the other. Doesn't mean that the two variables are related
# coeff 0 -> no relationship
# less variables to fit b/c NA

country_list <- primary_school %>% 
  select(1:1) %>%
  distinct() 

save(country_list,file="country_list.Rda") 
  

## Map of World Educational Attainment
world <- primary_school %>%
  filter(year==c("2014","2015")) %>% 
  distinct(country)

map.world <- map_data("world")

world %>% 
  ggplot() +
  geom_map(data = map.world, map = map.world, 
           aes(map_id = region), fill = "white", color = "black") + 
  geom_map(aes(map_id = country), map = map.world) +
  expand_limits(x = map.world$long, y = map.world$lat) + # scale for fill
  theme_map() 

worldmap <- get_stamenmap(
    bbox = c(left = -180, bottom = -57, right = 179, top = 82.1), 
    maptype = "toner-background",
    zoom = 2
)

ggmap(worldmap)

primary_school %>%
  ggplot(aes(x=year, y=primary_school, color=high_gdp)) + 
  geom_jitter() +
  facet_wrap(~ high_gdp) +
  geom_smooth(se=FALSE)

plot <- ggplot(primary_school, aes(x=gdp_capita, y=primary_school, color=high_gdp)) + 
  geom_point() + 
  geom_smooth(method = "lm", se = FALSE) +
  labs(x="GDP per capita", y="Educational Attainment (Primary School)", 
       title="Educational Attainment and GDP per Capita by Continent")

ggplotly(plot)

NA

##Schooling Cost Graph

ggplot(primary_school,aes(x=schooling_cost,y=primary_school, color=high_gdp))+
  geom_point()+
  geom_smooth(method="lm", se=FALSE)

Gini